Articles | Volume 16, issue 15
Development and technical paper
10 Aug 2023
Development and technical paper |  | 10 Aug 2023

Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model

Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik

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Cited articles

Agarwal, N., Kondrashov, D., Dueben, P., Ryzhov, E., and Berloff, P.: A Comparison of Data-Driven Approaches to Build Low-Dimensional Ocean Models, J. Adv. Model. Earth Sy., 13, e2021MS002537,, 2021. a
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Arnold, H. M., Moroz, I. M., and Palmer, T. N.: Stochastic parametrizations and model uncertainty in the Lorenz’96 system, Philosophical Transactions of the Royal Society A: Mathematical, Phys. Eng. Sci., 371, 20110479,, 2013. a, b, c, d
Bahdanau, D., Cho, K., and Bengio, Y.: Neural machine translation by jointly learning to align and translate, arXiv [preprint],, 2014. a
Short summary
How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.